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Mathematics
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New Advances of Optimization and Data Envelopment Analysis
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New Advances of Optimization and Data Envelopment Analysis

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D: Statistics and Operational Research".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1140

Special Issue Editor

Dr. Nuria Ramón

E-Mail Website
Guest Editor
Center of Operations Research (CIO), Miguel Hernández University of Elche, Avda. de la Universidad, s/n, Alicante, 03202 Elche, Spain
Interests: data envelopment analysis; target setting; balanced efforts; benchmarking; closest targets

Special Issue Information

Dear Colleagues,

New advances in optimization and data envelopment analysis (DEA) are reshaping the way we evaluate efficiency and support decision-making in complex environments. Recent developments extend traditional DEA models by incorporating robust optimization, stochastic methods, machine learning, and network structures, providing powerful tools for real-world challenges. These innovations enable researchers and practitioners to better address uncertainty, improvement plans, and multi-layered systems in areas such as healthcare, energy, logistics, sustainability, finance, and education.

If you require an extension or have any questions regarding this Special Issue, please do not hesitate to contact me by replying to this email. Thank you for your time, and I look forward to receiving your contributions.

Considering your expertise in the field, I warmly encourage you to submit an article. If you are unable to contribute at this time, please feel free to share this invitation with your colleagues. Alternatively, you may provide us with the contact details of anyone who might be interested, and we will reach out to them directly.

Dr. Nuria Ramón
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data envelopment analysis
  • target setting
  • benchmarking
  • closest targets
  • improvement plans

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Published Papers (2 papers)

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Research

29 pages, 417 KB  
Article
The Operational Efficiency Measurement of China’s Top 100 Digital Economy Firms: An Approach Based on DEA and Kernel Density Estimation
by Linyan Zhang, Yumeng Zhang, Kun Yang and Jian Zhang
Mathematics 2026, 14(9), 1561; https://doi.org/10.3390/math14091561 - 5 May 2026
Viewed by 300
Abstract
In recent years, China’s digital economy has become a key engine for high-quality development. Assessing the operational efficiency of leading digital enterprises is crucial for optimizing resource allocation and promoting sectoral growth. However, existing research largely remains at regional or industry levels and [...] Read more.
In recent years, China’s digital economy has become a key engine for high-quality development. Assessing the operational efficiency of leading digital enterprises is crucial for optimizing resource allocation and promoting sectoral growth. However, existing research largely remains at regional or industry levels and typically reports efficiency scores without diagnosing the root sources of inefficiency. To fill this gap, this study measures the operational efficiency of 99 firms selected from China’s Top 100 Digital Economy list (2017–2022) using the BCC-DEA model, and analyzes their dynamic evolution via kernel density estimation. The findings reveal a fluctuating upward trend in overall efficiency, and that the gap in overall technical efficiency primarily originates from scale efficiency rather than pure technical efficiency. The kernel density peak exhibits a “rise–decline–rise” pattern, indicating existing but narrowing efficiency differences among firms. By decomposing efficiency, this study further classifies firms into four types, revealing that inefficiency is heterogeneous. This paper makes three main contributions. First, it identifies scale efficiency as the main source of efficiency gaps. Second, it classifies firms into four types, revealing that inefficiency is heterogeneous. Third, it uses kernel density estimation to track the dynamic evolution of efficiency, showing a narrowing efficiency gap but a persistent superstar effect. Two policy implications follow: firms with low pure technical efficiency should focus on management training and technology adoption, while firms with low scale efficiency should pursue scale expansion through mergers or partnerships. Full article
(This article belongs to the Special Issue New Advances of Optimization and Data Envelopment Analysis)
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20 pages, 299 KB  
Article
A Pessimistic Two-Stage Network DEA Model with Interval Data and Endogenous Weight Restrictions
by Chia-Nan Wang and Giovanni Cahilig
Mathematics 2026, 14(5), 917; https://doi.org/10.3390/math14050917 - 8 Mar 2026
Viewed by 492
Abstract
This paper develops a pessimistic two-stage network data envelopment analysis (DEA) model that integrates interval-valued data and endogenous weight restrictions within a unified linear programming framework. The proposed approach explicitly captures internal network structures while addressing bounded data uncertainty through an interval-to-deterministic transformation [...] Read more.
This paper develops a pessimistic two-stage network data envelopment analysis (DEA) model that integrates interval-valued data and endogenous weight restrictions within a unified linear programming framework. The proposed approach explicitly captures internal network structures while addressing bounded data uncertainty through an interval-to-deterministic transformation that preserves linearity and avoids probabilistic assumptions. Robustness is interpreted in the pessimistic interval DEA sense, where efficiency is evaluated under worst-case realizations of observed bounds rather than through explicit uncertainty-set optimization. To mitigate weight degeneracy and enhance discrimination power, data-driven proportional weight restrictions are introduced; these endogenous bounds are constructed solely from observed data and regularize the multiplier space without relying on subjective preferences or tuning parameters, while maintaining scale invariance and the nonparametric nature of DEA. The model admits equivalent multiplier and envelopment formulations and enables meaningful decomposition of overall efficiency into stage-specific components. Fundamental theoretical properties—including feasibility, boundedness, monotonicity, efficiency decomposition, and special case consistency—are rigorously established. An empirical application to OECD macroeconomic data, accompanied by sensitivity evaluation, demonstrates the stability and discriminatory capability of the proposed framework under bounded variability. Computational analysis confirms that the model retains linear programming structure and exhibits linear growth in problem size with respect to the number of decision-making units, thereby preserving the scalability characteristics of classical two-stage network DEA formulations. The proposed framework provides a theoretically grounded and computationally tractable approach for network efficiency analysis under bounded interval uncertainty. Full article
(This article belongs to the Special Issue New Advances of Optimization and Data Envelopment Analysis)
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